The present invention relates generally to automatic cognate detection and, more specifically, to automatic cognate detection in a computer-assisted language learning system.
Knowledge of a second language can be desirable for a variety of reasons. Individuals may desire to learn a second language to satisfy personal or career goals. Current techniques for computer-assisted language learning (CALL) include approaches such as translation and transcription exercises, simulated dialogue, reading in the target language, or reading parallel language texts. Generally speaking, these techniques present some sort of pure or combined audio, graphic, textual, or video stimulus to which the learner is to respond using speech, writing, or menu selections.
Contemporary linguistics research shows that language learning is strongly facilitated by the use of the target language in interactions where the learner can negotiate the meaning of vocabulary and that the use of words in new contexts stimulates a deeper understanding of their meaning. A challenge in systems for CALL relates to curation of learning material, namely how to select content most appropriate for a user. Typically, this is done manually by an instructor. Automatic data curation via retrieval from large mono- or multi-lingual archives is an emerging approach.
Natural-language processing (NLP) techniques infer the meaning of terms and phrases by analyzing their syntax, context, and usage patterns. Human language is so complex, variable (there are many different ways to express the same meaning), and polysemous (the same word or phrase may mean many things in different contexts) that NLP presents an enormous technical challenge. Decades of research have led to many specialized techniques each operating on language at different levels and on different isolated aspects of the language understanding task.